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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q134-Q139):

NEW QUESTION # 134
You are tasked with fine-tuning a pre-trained generative AI model for customer support automation. The goal is to enhance the model's performance in generating concise, relevant answers to frequently asked questions (FAQs). To do this, you need to optimize the prompt-tuning process.
Which two of the following techniques would be most effective for creating a prompt-tuned model for this purpose? (Select two)

A. Use a large set of domain-specific FAQs and fine-tune the model using those examples, ensuring that prompts are tailored to each type of question.
B. Introduce randomness in prompts by using variations in the wording for similar FAQs to improve the model's adaptability to different styles.
C. Limit the training data to 100 samples of FAQs to prevent overfitting and keep the prompt-tuning process computationally efficient.
D. Utilize reinforcement learning to penalize long or irrelevant responses during the tuning phase, optimizing the model for concise output.
E. Shorten the prompts to the minimum number of words needed to address the FAQ directly, focusing on the key terms that drive the correct output.

Answer: A,E

NEW QUESTION # 135
You are tasked with designing a prompt using a few-shot strategy for Watsonx AI to generate a legal contract summary. You provide two examples of contract summaries before asking the model to summarize a new contract.
Which of the following options best demonstrates an effective few-shot prompt for this task?

A. "Provide a summary of the following contract: [New Contract].
Example 1: [First Contract]
Summary: [First Contract Summary]
Example 2: [Second Contract]
Summary: [Second Contract Summary]."
B. "Example 1: [First Contract]
C. "Generate a summary for [New Contract]."
D. "Example 1: [First Contract] Summary: [First Contract Summary]
Example 2: [Second Contract] Summary: [Second Contract Summary]
Generate a summary of the following contract: [New Contract]."

Answer: D

NEW QUESTION # 136
You're developing a generative AI system for a medical diagnosis application that uses patient data. Your responsibility includes designing prompts that extract valuable insights without exposing sensitive patient information.
Which of the following steps is the most effective way to reduce model risks related to privacy while ensuring useful outputs from the AI?

A. Restrict the model's output length to reduce the risk of sensitive information leakage.
B. Increase the length of the prompts to provide more context, ensuring more accurate results.
C. Utilize a smaller model to minimize the likelihood of overfitting sensitive data.
D. Employ differential privacy techniques to add noise to the model's outputs.

Answer: D

NEW QUESTION # 137
You are working with IBM Watsonx to generate automated customer support responses. To ensure consistency and flexibility in responses across multiple product categories, you decide to use prompt variables.
Which of the following best describes the benefits of using prompt variables in this scenario?

A. They automatically improve the accuracy of the AI's responses by allowing the system to learn from each generated prompt.
B. They ensure that the AI generates responses with consistent tone and personality across all prompts, regardless of product category.
C. They enable the AI to better predict the intent of the customer query, reducing the need for explicit customer input.
D. They allow for dynamic input fields in prompts, making it easier to tailor responses for different product categories without rewriting the entire prompt.

Answer: D

NEW QUESTION # 138
You are developing a generative AI model using the IBM Watsonx platform to assist in customer service. While the model's responses are highly accurate, there is concern that the model may inadvertently expose personal information (PII) or sensitive data during interactions. As a responsible AI engineer, it is crucial to mitigate this risk.
Which of the following is the most critical risk associated with the exposure of personal information in generative AI models?

A. The model can generate outputs that are too general, failing to meet the specific needs of the user.
B. The model can generate overly creative or non-factual responses, leading to brand reputation damage.
C. The model can unintentionally memorize and regurgitate personal information from the training data, leading to privacy violations.
D. The model might produce content that doesn't align with the cultural preferences of the user.

Answer: C

NEW QUESTION # 139
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q134-Q139):

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